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sub:assertion {
  <https://doi.org/10.48550/arXiv.2508.14427> dct:title "Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models";
    <http://purl.org/spar/cito/describes> <https://neverblink.eu/ontologies/llm-kg/methods#KnowledgeGraphInfusedFineTuningFramework>;
    <http://purl.org/spar/cito/discusses> <https://neverblink.eu/ontologies/llm-kg/methods#DRAGON>,
      <https://neverblink.eu/ontologies/llm-kg/methods#KGLM>, <https://neverblink.eu/ontologies/llm-kg/methods#KGSFT>;
    a prov:Entity .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#DRAGON> a <http://purl.org/spar/fabio/Workflow>;
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  <https://neverblink.eu/ontologies/llm-kg/methods#KGLM> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "KGLM" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#KGSFT> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "KG-SFT" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#KnowledgeGraphInfusedFineTuningFramework>
    dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMPretraining>;
    a <http://purl.org/spar/fabio/Workflow>;
    rdfs:comment "This method proposes a fine-tuning algorithm framework that injects knowledge graph information into large language models during the fine-tuning stage. It uses a GNN to encode KG information, a fusion mechanism to combine KG embeddings with LLM representations, a gating mechanism to balance contributions, and a joint loss function. The primary goal is to improve the LLM's knowledge expression and structured reasoning capabilities.";
    rdfs:label "Knowledge Graph-Infused Fine-Tuning Framework";
    <https://neverblink.eu/ontologies/llm-kg/hasTopCategory> <https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM> .
}

sub:provenance {
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    prov:wasDerivedFrom <https://doi.org/10.48550/arXiv.2508.14427> .
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